| A tail-index analysis of stochastic gradient noise in deep neural networks U Simsekli, L Sagun, M Gurbuzbalaban International Conference on Machine Learning, 5827-5837, 2019 | 194 | 2019 |
| Why random reshuffling beats stochastic gradient descent M Gürbüzbalaban, A Ozdaglar, PA Parrilo Mathematical Programming 186, 49-84, 2021 | 179 | 2021 |
| On the convergence rate of incremental aggregated gradient algorithms M Gurbuzbalaban, A Ozdaglar, PA Parrilo SIAM Journal on Optimization 27 (2), 1035-1048, 2017 | 159 | 2017 |
| The heavy-tail phenomenon in SGD M Gurbuzbalaban, U Simsekli, L Zhu International Conference on Machine Learning, 3964-3975, 2021 | 85 | 2021 |
| Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration X Gao, M Gurbuzbalaban, Z Lingjiong arXiv preprint arXiv:1809.04618, 2018 | 76* | 2018 |
| Fast Approximation of the Norm via Optimization over Spectral Value Sets N Guglielmi, M Gürbüzbalaban, ML Overton SIAM Journal on Matrix Analysis and Applications 34 (2), 709-737, 2013 | 59 | 2013 |
| Global convergence rate of proximal incremental aggregated gradient methods ND Vanli, M Gurbuzbalaban, A Ozdaglar SIAM Journal on Optimization 28 (2), 1282-1300, 2018 | 58 | 2018 |
| Convergence rate of incremental gradient and incremental Newton methods M Gurbuzbalaban, A Ozdaglar, PA Parrilo SIAM Journal on Optimization 29 (4), 2542-2565, 2019 | 57 | 2019 |
| A globally convergent incremental Newton method M Gürbüzbalaban, A Ozdaglar, P Parrilo Mathematical Programming 151 (1), 283-313, 2015 | 54 | 2015 |
| Robust accelerated gradient methods for smooth strongly convex functions NS Aybat, A Fallah, M Gurbuzbalaban, A Ozdaglar SIAM Journal on Optimization 30 (1), 717-751, 2020 | 53 | 2020 |
| A universally optimal multistage accelerated stochastic gradient method NS Aybat, A Fallah, M Gurbuzbalaban, A Ozdaglar Advances in neural information processing systems 32, 2019 | 49 | 2019 |
| Fractional underdamped langevin dynamics: Retargeting sgd with momentum under heavy-tailed gradient noise U Simsekli, L Zhu, YW Teh, M Gurbuzbalaban International conference on machine learning, 8970-8980, 2020 | 46 | 2020 |
| First exit time analysis of stochastic gradient descent under heavy-tailed gradient noise TH Nguyen, U Simsekli, M Gurbuzbalaban, G Richard Advances in neural information processing systems 32, 2019 | 43 | 2019 |
| Surpassing gradient descent provably: A cyclic incremental method with linear convergence rate A Mokhtari, M Gurbuzbalaban, A Ribeiro SIAM Journal on Optimization 28 (2), 1420-1447, 2018 | 42 | 2018 |
| Accelerated linear convergence of stochastic momentum methods in wasserstein distances B Can, M Gurbuzbalaban, L Zhu International Conference on Machine Learning, 891-901, 2019 | 38 | 2019 |
| When cyclic coordinate descent outperforms randomized coordinate descent M Gurbuzbalaban, A Ozdaglar, PA Parrilo, N Vanli Advances in Neural Information Processing Systems 30, 2017 | 38 | 2017 |
| On Nesterov’s nonsmooth Chebyshev–Rosenbrock functions M Gürbüzbalaban, ML Overton Nonlinear Analysis: Theory, Methods & Applications 75 (3), 1282-1289, 2012 | 34 | 2012 |
| Mert Gürbüzbalaban, Thanh Huy Nguyen, Gaël Richard, and Levent Sagun. On the heavy-tailed theory of stochastic gradient descent for deep neural networks U Simsekli arXiv preprint arXiv:1912.00018, 3, 2019 | 30 | 2019 |
| Robust distributed accelerated stochastic gradient methods for multi-agent networks A Fallah, M Gürbüzbalaban, A Ozdaglar, U Şimşekli, L Zhu The Journal of Machine Learning Research 23 (1), 9893-9988, 2022 | 28 | 2022 |
| Convergence rates of stochastic gradient descent under infinite noise variance H Wang, M Gurbuzbalaban, L Zhu, U Simsekli, MA Erdogdu Advances in Neural Information Processing Systems 34, 18866-18877, 2021 | 27 | 2021 |
Lingjiong ZhuFlorida State UniversityVerified email at fsu.edu
